激光角度欺骗仿真系统的软件可靠性指标分配

在激光角度欺骗仿真系统的需求分析和概要设计阶段,使用基于模糊故障模式、影响及危害性分析(FMECA)框架的分配法对该系统软件可靠性分配。首先建立影响因素集、评价集合;然后基于FMECA框架进行模糊综合评价;再利用层次分析法(AHP)得到权重集;最后通过量化手段得到各子系统的不可靠系数并将它们转化为平均无故障时间(MTBF)。最终得到的可靠性指标符合专家评估和类似软件系统的可靠性分配结果。     

Coverage Modeling and Reliability Analysis Using Multi-state Function

Fault tree analysis is an effective method for predicting the reliability of a system.It gives a pictorial representation and logical framework for analyzing the reliability.Also,it has been used for a long time as an effective method for the quantitative and qualitative analysis of the failure modes of critical systems.In this paper,we propose a new general coverage model (GCM) based on hardware independent faults.Using this model,an effective software tool can be constructed to detect,locate and recover fault from the faulty system.This model can be applied to identify the key component that can cause the failure of the system using failure mode effect analysis (FMEA).     

绕组温升测量方法研究

本文介绍家用电器中的绕组温升测量的一般方法，提出认真研究的重要性。     

Information Theoretic Weighted Fuzzy Clustering Ensemble

In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM), Reliability Based Graph Partitioning (RBGP) and Reliability Based Hyper Clustering (RBHC) as three new fuzzy clustering consensus functions. Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation. Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble. To do so, the new metric is defined to estimate the fuzzy cluster unreliability; then, the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator (RDCI). The time complexities of RBHC and RBGP are linearly proportional with the number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.     

计算机软件技术的不可靠性分析

计算机技术发展十分迅速,近年来,计算机软件技术更有着突飞猛进的发展。对于软件技术而言,其优势非常显著。甚至在一段时间里,存在硬件技术与软件技术无法协调发展的境况--计算机硬件技术无法与软件技术进行匹配。正在人们将注意力关注到计算机软件技术的时候,其弊端开始显现出来。软件技术虽然发展空间巨大,优势突出,但是其不可靠性也是存在的。文章将重点分析计算机软件技术的不可靠性。     

网络编码下不可靠自组织网络高效路由算法

无线自组织网络(wireless ad hoc network)中链路的不可靠性与高丢包率是影响网络性能和应用的主要缺陷.为了优化多跳的不可靠无线网络中完成数据传输所需的数据分组发送次数,提出了融合路径切换思想的基于网络编码的路由协议(network coding routing with path switching,简称NCPS).为了发现潜在的适合网络编码的路径,首先分析并提出了网络中路径可编码与可解码条件;其次对比于传统的最优路径,分析得出了在网络编码下路径切换所能获取的编码收益;最后以优化网络中数据分组发送次数为目标,设计了网络编码下结合路径切换的路由协议.实验结果表明,在不同的网络环境参数下,NCPS能够有效减少网络中的数据发送次数,获取稳定的编码收益.     

航空发动机需求预测

航空发动机价格昂贵、故障较多,保障人员仍采用经验预测需求,造成发动机短缺或者库存过度积压的问题经常发生。本文提出从到寿和随机故障产生的需求出发分析建立航空发动机的需求预测模型,到寿产生的需求通过装机发动机的规定寿命、剩余寿命、单机飞行任务等计算,随机故障产生的需求通过损坏频率和不可靠度2种方法计算。算例表明航空发动机需求预测模型具有一定的准确性和实用性。     

Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks

In wireless sensor networks, most data aggregation scheduling methods let all nodes aggregate data in every time instance. It is not energy efficient and practical because of link unreliability and data redundancy. This paper proposes a lossy data aggregation (LDA) scheme to reduce traffic and save energy. LDA selects partial child nodes to sample data at partial time slots and allows estimated aggregation at parent nodes or a root in a network. We firstly consider that all nodes sample data synchronously and find that the error between the real value of a physical parameter and that measured by LDA is bounded respectively with and without link unreliability. Detailed analysis is given on error bound when a confidence level is previously assigned to the root by a newly designed algorithm. Thus, each parent can determine the minimum number of child nodes needed to achieve its assigned confidence level. We then analyze a probability to bound the error with a confidence level previously assigned to the root when all nodes sample data asynchronously. An algorithm then is designed to implement our data aggregation under asynchronization. Finally, we implement our experiment on the basis of real test‐beds to prove that the scheme can save more energy than an existing algorithm for node selection, Distributive Online Greedy (DOG). Copyright © 2012 John Wiley & Sons, Ltd.     

The notion of a probabilistic cellular acceptor

The combination of the notions of a cellular automaton and a probabilistic automaton, called a probabilistic cellular automaton, was proposed in 1973 by the first author of this paper as a more adequate model of any system with unreliable components that operate in a parallel manner. In this paper a language-acceptor type of such a probabilistic cellular automaton, called aprobabilistic bounded cellular acceptor (PBCA), is defined and studied. It is shown that the class of all languages accepted by one-dimensional PBCAs includes both the class of all languages accepted by bounded cellular acceptors (BCAs) and the class of all languages accepted by probabilistic acceptors (PAs). Also, it is shown that every language accepted by ad-dimensional PBCA at a given cut point is accepted by ad-dimensional PBCA at an arbitrary nonzero cut point. The class of all languages accepted by one-dimensional PBCAs at cut point 0 is shown to be precisely the class of all context-sensitive languages. Several decision problems for PBCAs are shown to be recursively unsolvable. Finally, various open problems concerning PBCAs and PBCLs are discussed.     

Tracing the unreliability and recognizing the major unreliability contribution of network components

Weak part analysis of a system is a key element in a system reliability quantification process. It enables the weakest areas of a system to be recognized and assists in directing remedial measures for improving the system reliability. This paper presents a novel approach to identifying the weak parts using the unreliability tracing (UT) technique and introduces the proportional sharing principle (PSP). The model for tracing the unreliability of a complex network is presented based on reliability evaluation methods using minimal cut sets (MCSs) and the PSP. The system UT sharing factors (UTSFs) are derived to easily identify the major unreliability contributions (MUCs) in a system. The method is illustrated using three cases and the UT, UTSF and the reliability impact analysis of different components are discussed. The results show that the developed technique can be applied to complex networks for UT tracing and recognizing the MUC.